Une ontologie de la culture de la vigne : des savoirs académiques aux savoirs d'expérience

Dans le cadre d’un projet FUI initié en octobre 2016 (projet winecloud) visant à construire un outil de traçabilité et prédictif du cycle de la vigne et du vin, un travail sur la collecte et la nature des savoirs a été nécessaire de manière à penser un système ontologique qui se rapproche le plus du raisonnement du domaine métier. Le présent article vise plus spécifiquement à étudier le cycle de vie de la vigne. Nous rendons compte que les savoirs académiques présents dans les sources théoriques et scientifiques s’ajustent, se réactualisent à la lumière des savoirs d’expérience des viticulteurs. Ce travail s’attache également à analyser la nature protéiforme des savoirs d’expérience et à rendre compte de leur pluralité.Dans le cadre d’un projet FUI initié en octobre 2016 (projet winecloud) visant à construire un outil de traçabilité et prédictif du cycle de la vigne et du vin, un travail sur la collecte et la nature des savoirs a été nécessaire de manière à penser un système ontologique qui se rapproche le plus du raisonnement du domaine métier. Le présent article vise plus spécifiquement à étudier le cycle de vie de la vigne. Nous rendons compte que les savoirs académiques présents dans les sources théoriques et scientifiques s’ajustent, se réactualisent à la lumière des savoirs d’expérience des viticulteurs. Ce travail s’attache également à analyser la nature protéiforme des savoirs d’expérience et à rendre compte de leur pluralité

Stance detection on social media: State of the art and trends

Stance detection on social media is an emerging opinion mining paradigm for various social and political applications in which sentiment analysis may be sub-optimal. There has been a growing research interest for developing effective methods for stance detection methods varying among multiple communities including natural language processing, web science, and social computing. This paper surveys the work on stance detection within those communities and situates its usage within current opinion mining techniques in social media. It presents an exhaustive review of stance detection techniques on social media, including the task definition, different types of targets in stance detection, features set used, and various machine learning approaches applied. The survey reports state-of-the-art results on the existing benchmark datasets on stance detection, and discusses the most effective approaches. In addition, this study explores the emerging trends and different applications of stance detection on social media. The study concludes by discussing the gaps in the current existing research and highlights the possible future directions for stance detection on social media.Comment: We request withdrawal of this article sincerely. We will re-edit this paper. Please withdraw this article before we finish the new versio

Towards a tweets contextualization based on conversation graphs analysis

Même avec le récent passage à 280 caractères, les messages de Twitter considérés dans leur singularité, sans information additionnelle exogène, peuvent confronter leurs lecteurs à des difficultés d’interprétation. L’ajout d’une contextualisation à ces messages s’avère donc une voie de recherche prometteuse pour faciliter l’accès à leur contenu informationnel. Dans la dernière décennie, la majorité des travaux se sont concentrés sur la construction de résumés à partir de sources d’information complémentaires telles que Wikipédia. Nous avons choisi dans cette thèse une voie complémentaire différente qui s’appuie sur l’analyse des conversations sur Twitter afin d’extraire des informations utiles à la contextualisation d’un tweet. Ces informations ont été intégrées dans un prototype qui, pour un tweet donné, propose une visualisation d’un sous-graphe du graphe de conversation associé au tweet. Ce sous-graphe extrait automatiquement à partir de l’analyse des distributions des indicateurs structurels, permet de mettre en évidence notamment des individus qui jouent un rôle majeur dans la conversation et des tweets qui ont contribué à la dynamique des échanges. Ce prototype a été testé sur un panel d’utilisateurs, pour valider son apport et ouvrir des perspectives d’amélioration.Even with the recent switch to 280 characters, Twitter messages considered in their singularity, without any additional exogenous information, can confront their readers with difficulties of interpretation. The integration of contextualization on these messages is therefore a promising avenue of research to facilitate access to their information content. In the last decade, most works have focused on building summaries from complementary sources of information such as Wikipedia. In this thesis, we choose a different complementary path that relies on the analysis of conversations on Twitter in order to extract useful information for the contextualization of a tweet. These information were integrated in a prototype which, for a given tweet, offers a visualization of a subgraph of the conversation graph associated with the tweet. This subgraph, automatically extracted from the analysis of structural indicators distributions, allows to highlight particular individuals who play a major role in the conversation and tweets that have contributed to the dynamics of exchanges. This prototype was tested on a panel of users to validate its efficiency and open up prospects for improvement

Social Users Interactions Detection Based on Conversational Aspects

International audienceLast years, people are becoming more communicative through expansion of services and multi-platform applications such as blogs, forums and social networks which establishes social and collabo-rative backgrounds. These services like Twitter, which is the main domain used in our work can be seen as very large information repository containing millions of text messages usually organized into complex networks involving users interacting with each other at specific times. Several works have proposed tools for tweets search focused only to retrieve the most recent but relevant tweets that address the information need. Therefore, users are unable to explore the results or retrieve more relevant tweets based on the content and may get lost or become frustrated by the information overload. In addition, finding good results concerning the given subjects needs to consider the entire context. However, context can be derived from user interactions. In this work, we propose a new method to retrieval conversation on mi-croblogging sites. It's based on content analysis and content enrichment. The goal of our method is to present a more informative result compared to conventional search engine. The proposed method has been implemented and evaluated by comparing it to Google and Twitter Search engines and we obtained very promising results

Conversation Analysis on Social Networking Sites

International audienceWith the explosion of Web 2.0, people are becoming more communicative through expansion of services and multi-platform applications such as microblogs, forums and social networks which establishes social and collabora-tive backgrounds. These services can be seen as very large information repository containing millions of text messages usually organized into complex networks involving users interacting with each other at specific times. Several works focused only to retrieve separate tweets or those sharing same hashtags, but, it is not powerful enough if the goal of the search is to retrieve relevant tweets based on content. In addition, finding good results concerning the given subjects needs to consider the entire context. However, context can be derived from user interactions. In this work, we propose a new method to retrieval conversation on microblogging sites. It's based on content analysis and content enrichment. The goal of our method is to present a more informative result compared to conventional search engine. To valid our method, we developed the TCOND system (Twitter Conversation Detector) which offers an alternative, results to keyword search on twitter and google. We have evaluated our method on collected social network corpus related to specific subjects, and we obtained good results

Exploring Big Data Environment for Conversation Data Analysis and Mining on Microblogs

International audienceToday, social media services and multiplatform applications such as microblogs, forums and social networks gives people the ability to communicate, interact and generate content which establish social and collaborative backgrounds. These services now embodies the leading and biggest repository containing millions of Big social Data that can be useful for many applications such as measure public sentiment, trends monitoring, reputation management and marketing campaigns. But social media data are essentially unstructured that's what makes it so interesting and so hard to analyze. Making sense of it and understanding what it means will require all new technologies and techniques, including the emerging field of big data. In addition, social media is a key model of the velocity and variety which are main characteristics of Big Data. In this paper, we propose a new approach to retrieve conversation on microblogging sites that combine Big Data environment and social media analytics solutions. The goal of our approach is to present a more informa-tives result and solve the information overload problem within Big Data environment. The proposed approach has been implemented and evaluated by comparing it with Google and Twitter Search engines and we obtained very promising results

Illumination3.0: A Semantic Annotation Platform Based on Ontology for Medieval Illuminations

National audienc

An Ontology-Based Monitoring System in Vineyards of the Burgundy Region

International audienceGiven the France's rich wine heritage as well as its pioneering position as the world's second wine producer, the production of high quality wines plays a role of primary importance. The recent development of IOT and efficient big data processing has been shown to provide purposeful issue to permanent monitoring during the entire wine making process. Standing within this trend, we introduce in this paper an intelligent system for vineyards monitoring in the Burgundy region. The main trust of the proposed system relies on the use of the Swrl rules in WineCloud ontology. The design of the ontology is mainly based on information gathered from interviews with wine growers. In addition, sensor data is also collected and used to feed the ontology after being processed. The system is used in the aim to have better grape quality with an improved vineyard management. To do so, association rules are extracted from the collected data aiming to provide useful knowledge to forecast vine diseases

WINECLOUD: Une ontologie d'événements pour la modélisation sémantique des données de capteurs hétérogènes

International audienc

De la science à l’expérience, la pluralité des savoirs en viticulture : le cas des maladies

International audienc